i make the models that drive cars run fast enough to actually drive cars. most of my time goes into fitting end-to-end neural networks within hard latency budgets on custom silicon. milliseconds matter when the car is moving.

mostly i think about what sits between the math and the metal. been reading a lot of interpretability research lately, trying to understand what these models actually learn. not just how to make them faster.